Multi-class classifiers take the approaches of one-versus-all or all-versus-all or their variations, which train multiple models for multiple classes and pick a winning class according to the inference results from the multiple models for a classification instance. The requirement for multiple models can make these approaches more complex to implement than binary classification algorithms.